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JR38031 INTERN - Machine learning based Scatterometry Optimization
Location :
1, North Coast Drive, Singapore 757432
Department :
Product Integration Engineering (PIE), Metrology
Title of project:
I mprovement plan on existing ML-based scatterometry framework and develop solutions in Python.\xe2\x80\x8b
Project Description :
Scatterometry is a technique that uses light to measure critical dimension of a post-etch structure during wafer fabrication. Machine Learning (ML)-based scatterometry uses supervised learning model to evaluate the measurement value from the light information. Users run scripts to train these ML models, and the models are then deployed in production environment for real-time measurement evaluation. There is a constant need to improve the learning algorithm, improve user experience on model training and reduce lag in real-time evaluation.
Scope:
The student will be introduced to scatterometry measurement technique. He/She will be able to learn the application of supervised learning technique and building a deployment pipeline in a manufacturing environment. The student will study the existing ML-based scatterometry framework and apply object-oriented programming as well as other Python scientific libraries, including Pandas and SciPy to the framework.
Deliverable:
To propose improvement plan on existing ML-based scatterometry framework and develop solutions in Python.
Skillset Required :
eQuest
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